Transcript Slide 1

Automated extraction of beach
bathymetries from video images
Laura Uunk
MSc Thesis
prof. dr. S.J.M.H. Hulscher
dr. K.M.Wijnberg
ir. R. Morelissen
Contents
Beach bathymetries by shoreline mapping
Manually mapping shorelines (IBM)
Automatically mapping shorelines (ASM)
Problems encountered
Automated quality control
Automatically vs. manually obtained bathymetries
Beach behaviour
Conclusions
2
Beach bathymetries by shoreline mapping
Argus images
• Time exposure images
 10 minute average
• Every half hour
Beach bathymetry mapped
• Shoreline location
• Shoreline elevation
• Throughout tidal cycle
• Elevation data between
low and high water
Timex image of Egmond Coast 3D site, camera 1
3
Manually mapping shorelines (IBM)
Interface of the Intertidal Beach Mapper (IBM)
4
Manually mapping shorelines (IBM)
Requires many man-hours
• up to 4 hours for one day for one station (5 cameras)
Therefore no daily bathymetries, but monthly
Opportunities of Argus not completely used
Automated version was developed (ASM)
• Plant
• Cerezo and Harley Dutch beach
5
Automatically mapping shorelines (ASM)
Human steps are automated
• Definition of the region of interest
> based on expected shoreline
location on bench-mark
bathymetry
• Quality control
> compare detected points
against bench-mark
bathymetry
Bench-mark bathymetry
6
Automatically mapping shorelines (ASM)
Database with
shoreline points
start / next time step
Shoreline points
within time window
Elevation model
Bench-mark bathymetry
Shoreline elevation
Region of interest
Detection method
Detected shoreline points
Acceptance criterion
Accepted
shoreline points
7
Problems encountered
Bad bench-mark bathymetry
> bad definition ROI
> bad quality control
 Start of a downward spiral
Bad bench mark bathymetry
8
Problems encountered - downward spiral
Database with
shoreline points
start / next time step
Shoreline points
within time window
Elevation model
Bench-mark bathymetry
Shoreline elevation
Region of interest
Detection method
Detected shoreline points
Acceptance criterion
Accepted
shoreline points
9
Problems encountered - solutions
Better definition of the Region of Interest
• large smoothing scales loess interpolation
> better expected shoreline location
• extension to edge of image
> inclusion of entire shoreline
• avoid zigzagging
> inclusion of entire shoreline
10
Problems encountered - solutions
small smoothing scales
short time window
larger smoothing scales  Better expected
shoreline location
longer time window
11
Problems encountered - solutions
Better definition of the Region of Interest
• large smoothing scales loess interpolation
> better expected shoreline location
• extension to edge of image
> inclusion of entire shoreline
• avoid zigzagging
> inclusion of entire shoreline
12
Problems encountered - solutions
13
Problems encountered - solutions
Double quality control
• Two bench-mark bathymetries
> 1: small smoothing scales, small time window
> 2: large smoothing scales, large time window
Shoreline points first compared to first bathymetry
Points that could not be checked are then compared to
second bathymetry
14
Problems encountered - solutions
small smoothing scale
 more detail
 more gaps
large smoothing scale
 less detail
 less gaps
15
Automated quality control
Fixed vertical criterion: Zdif
• Sometimes accept points
that are wrong
• Sometimes reject points
that are good
16
Automated quality control
 What value should be used?
ASM was run with three values for Zdif
• 0.10 m;
• 0.25 m;
• 0.50 m
ASM bathymetries compared to IBM bathymetries
• Coastal State Indicators (CSIs)
> Contours (-0.50 m NAP; 0 m NAP; 0.50 m NAP)
> MICL
17
Automated vs. manual
0 m contour for May 7th to 12th 2006
IBM
0.10 m
0.25 m
18
0.50 m
continued 0.25 m
Automated vs. manual
0.10 m
0.25 m
0.50 m
 No real differences for the different values of Zdif
19
continued 0.25 m
Automated vs. manual – in time
20
Beach behaviour
21
Conclusions
• Man-hours are saved by automatically mapping shorelines
• Results automated version (ASM) correspond well with results
manual version (IBM)
• 0 m contour by ASM shows immediate response of the beach to
changes in wave height
• this was not visible with monthly IBM bathymetries
• Opportunities provided by half-hourly Argus images can now be
fully exploited
• ASM data could be used to e.g.
• study storm impact
• study influence of nourishments
• support management decisions
22
Questions
23